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基于数据增强深度学习的苹果花检测方法研究

花匠小妙招
2025-04-26 21:39

摘要： 为提高现代化果园化学疏花的工作效率以及促进疏花机械的研发，针对果园生产中传统识别方法检测开花强度不全面、效率低等问题，提出一种基于数据增强YOLOv4深度学习的苹果花检测方法。首先，搭建YOLOv4网络模型，以CSPDarknet53框架为主干特征提取网络，然后为了减少样本数据不均和数量不足的影响，进行离线和在线数据增强的YOLOv4方法研究。最后，利用人工标注的苹果花图像对模型进行微调和训练，将所提出的方法与YOLOv3、YOLOv4和Faster R-CNN的检测模型进行对比试验，并讨论了不同拍摄情况下苹果花的检测性能，验证了该方法的有效性。试验结果表明，所提出的数据增强的YOLOv4的方法检测苹果花的AP值为99.76%；分别比Faster R-CNN、YOLOv3和YOLOv4提高了2.53%、14.56%和5.08%。相较于其他检测方法，该方法具有更高的苹果花检测精准度。

关键词: 苹果花检测, 深度学习, 数据增强, YOLOv4, 开花强度, 现代化果园

Abstract: In order to improve the efficiency of chemical flower thinning in modern orchards, promote the development of flower thinning machinery, as well as to address the problems of incomplete and low efficiency of traditional identification methods for detecting flowering intensity in orchard production, a dataenhanced YOLOv4 deep learningbased apple flower detection method was proposed. First, a YOLOv4 network model was built with the CSPDarknet53 framework as the backbone feature extraction network, and then offline and online data augmented YOLOv4 methods were investigated in order to reduce the effects of uneven sample data and insufficient quantity. Finally, the artificially annotated apple flower images were used to finetune and train the model, and the proposed method was compared with the detection models of YOLOv3, YOLOv4 and Faster R-CNN, after which the detection performance of apple flower under different shooting situations was discussed. The experimental results showed that the AP value of the proposed dataenhanced YOLOv4 method for detecting apple flower was 99.76%. It improved 2.53%, 14.56% and 5.08% over Faster R-CNN, YOLOv3 and YOLOv4, respectively. Compared with other detection methods, this method has higher precision in apple flower detection.

Key words: apple flower detection, deep learning, data enhancement, YOLOv4, flowering intensity, modern orchard

中图分类号:

S24： TP391.4